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Google Scholar is a freely accessible web search engine that indexes the full text or metadata of scholarly literature across an array of publishing formats and disciplines. . Released in beta in November 2004, the Google Scholar index includes peer-reviewed online academic journals and books, conference papers, theses and dissertations, preprints, abstracts, technical reports, and other ...
Ranking of query is one of the fundamental problems in information retrieval (IR), [1] the scientific/engineering discipline behind search engines. [2] Given a query q and a collection D of documents that match the query, the problem is to rank, that is, sort, the documents in D according to some criterion so that the "best" results appear early in the result list displayed to the user.
In information retrieval, Okapi BM25 (BM is an abbreviation of best matching) is a ranking function used by search engines to estimate the relevance of documents to a given search query. It is based on the probabilistic retrieval framework developed in the 1970s and 1980s by Stephen E. Robertson , Karen Spärck Jones , and others.
Free online search; offline use by subscription Golm Metabolome Database [67] Google Scholar: Multidisciplinary Free Google [68] HCI Bibliography: Human-computer interface: An electronic bibliography for most of HCI for researchers, developers, educators, and students Free Gary Perlman [69] HubMed: Medicine
A search engine called "RankDex" from IDD Information Services, designed by Robin Li in 1996, developed a strategy for site-scoring and page-ranking. [15] Li referred to his search mechanism as "link analysis," which involved ranking the popularity of a web site based on how many other sites had linked to it. [16]
Advanced search options in various search engines (like DuckDuckGo or Google) can help to pinpoint coverage about topics. To narrow searches to specific sites, here's something that works in DuckDuckGo and Google searches (be sure to include the topic in quotation marks): "Search topic" site:www.siteexample.com This generates results only from ...
Learning to rank [1] or machine-learned ranking (MLR) is the application of machine learning, typically supervised, semi-supervised or reinforcement learning, in the construction of ranking models for information retrieval systems. [2] Training data may, for example, consist of lists of items with some partial order specified between items in ...
The resulting “link popularity” is a measure of the number and quality of links to a website. It is an integral part of a website's ranking in search engines. Search engines examine each of the links to a particular website to determine its value. Although every link to a website is a vote in its favor, not all votes are counted equally.